Early approaches to offensive language identification relied first on traditional machine learning classifiers [1]} and later on neural networks combined with word embeddings [2]}. Transformer-based models like BERT [3]} and ELMO [4]} have been recently applied to offensive language detection achieving competitive very competitive scores in recent SemEval competitions such as HatEval [5]} OffensEval [6]}.
